1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

1.2 MERSCOPE Data Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

1.2.1 NT28530x2, (Normal)

1.2.2 TT28519x1, (Tumor)

1.2.3 TT28519x2, (Tumor )

1.2.4 NT28530x1, (Normal)

1.3 Transcript Mis-Match on FOV Boundary

2.1 Cell Segmentation & Filtering

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.

2.1.1 Cell Count after Filtering

2.1.2 Transcript Count Violin

Transcript Count Violin After Filtering

2.1.3 Transcript Count Spatial Map

Transcript Count Spatial Map After Filtering

2.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of cells and colored by batch

Cell types & Regions

Astro, Astrocyte;

ABC, arachnoid barrier cells;

BAM, border-associated macrophages;

BLA, Basolateral amygdala;

CB, cerebellum;

CGE, caudal ganglionic eminence;

CHOR, choroid plexus;

CNU, cerebral nuclei;

CR, Cajal–Retzius;

CT, corticothalamic;

CTX, cerebral cortex;

CTXsp, cortical subplate;

DC, dendritic cells;

DCO, dorsal cochlear nucleus;

DG, dentate gyrus;

EA, extended amygdala;

Endo, endothelial cells;

ENT, Entorhinal area;

ENTl, Entorhinal area, lateral part;

Epen, ependymal;

EPI, epithalamus;

ET, extratelencephalic;

GC, granule cell;

HB, hindbrain;

HPF, hippocampal formation;

HY, hypothalamus;

HYa, anterior hypothalamic;

IMN, immature neurons;

IT, intratelencephalic;

L6b, layer 6b;

LGE, lateral ganglionic eminence;

LH, lateral habenula;

LSX, lateral septal complex;

MB, midbrain;

MGE, medial ganglionic eminence;

MH, medial habenula;

MM, medial mammillary nucleus;

MY, medulla;

NN, non-neuronal;

NP, near-projecting;

NT, non-telencephalon;

OB, olfactory bulb;

OEC, olfactory ensheathing cells;

OLF, olfactory areas;

Oligo, oligodendrocytes;

OPC, oligodendrocyte precursor cells;

P, pons;

PAL, pallidum;

Peri, pericytes;

PIR, piriform cortex;

SMC, smooth muscle cells;

STR, striatum;

TE, telencephalon;

TH, thalamus;

UBC, unipolar brush cells;

VLMC, vascular leptomeningeal cells.

Neurotransmitter types

Chol, cholinergic;

Dopa, dopaminergic;

GABA, GABAergic;

Glut, glutamatergic;

Glyc, glycinergic;

Hist, histaminergic;

Nora, noradrenergic;

Sero, serotonergic

ADP, anterodorsal preoptic nucleus

AHN, anterior hypothalamic nucleus

ARH, arcuate hypothalamic nucleus

CLI, central linear nucleus raphe

CUN, cuneiform nucleus

DMH, dorsomedial nucleus of the hypothalamus

DMX, dorsal motor nucleus of the vagus nerve

IF, interfascicular nucleus raphe

LHA, lateral hypothalamic area

MDRN, medullary reticular nucleus

MPN, medial preoptic nucleus

MPO, medial preoptic area

MV, medial vestibular nucleus

NTS, nucleus of the solitary tract

PAG, periaqueductal grey

PARN, parvicellular reticular nucleus

PB, parabrachial nucleus

PBG, parabigeminal nucleus

PGRN, paragigantocellular reticular nucleus

PGRNd, paragigantocellular reticular nucleus, dorsal part

PH, posterior hypothalamic nucleus

PMv, ventral premammillary nucleus

PPN, pedunculopontine nucleus

PVa, periventricular hypothalamic nucleus, anterior part

PVHd, paraventricular hypothalamic nucleus, descending division

PVi, periventricular hypothalamic nucleus, intermediate part

PVpo, periventricular hypothalamic nucleus, preoptic part

PVR, periventricular region

RAmb, midbrain raphe nuclei

RL, rostral linear nucleus raphe

SBPV, subparaventricular zone

SNc, substantia nigra, compact part

SPIV, spinal vestibular nucleus

TMv, tuberomammillary nucleus, ventral part

VII, facial motor nucleus

VMPO, ventromedial preoptic nucleus

VTA, ventral tegmental area

ZI, zona incerta.